Overview

Brought to you by YData

Dataset statistics

Number of variables26
Number of observations2674
Missing cells14598
Missing cells (%)21.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 MiB
Average record size in memory925.6 B

Variable types

Numeric12
Text1
DateTime1
Categorical12

Alerts

cluster_k_4 has constant value "2" Constant
CUIT is highly overall correlated with Estado and 10 other fieldsHigh correlation
Estado is highly overall correlated with CUITHigh correlation
TipoSocietario is highly overall correlated with CUIT and 2 other fieldsHigh correlation
anio_preinscripcion is highly overall correlated with antiguedad and 1 other fieldsHigh correlation
antiguedad is highly overall correlated with anio_preinscripcion and 1 other fieldsHigh correlation
cant_Apoderado is highly overall correlated with cant_noAutenticado and 1 other fieldsHigh correlation
cant_MontoLimite is highly overall correlated with CUIT and 7 other fieldsHigh correlation
cant_antecedentes is highly overall correlated with CUIT and 3 other fieldsHigh correlation
cant_apercibimientos is highly overall correlated with CUIT and 4 other fieldsHigh correlation
cant_autenticado is highly overall correlated with cant_sinMontoLimiteHigh correlation
cant_noAutenticado is highly overall correlated with cant_Apoderado and 2 other fieldsHigh correlation
cant_procesos_adjudicado is highly overall correlated with cant_MontoLimite and 1 other fieldsHigh correlation
cant_representante is highly overall correlated with CUITHigh correlation
cant_sinMontoLimite is highly overall correlated with cant_Apoderado and 2 other fieldsHigh correlation
cant_socios is highly overall correlated with cant_MontoLimiteHigh correlation
cant_suspensiones is highly overall correlated with CUIT and 5 other fieldsHigh correlation
dcant_procesos_adjudicado is highly overall correlated with CUIT and 1 other fieldsHigh correlation
dmonto_total_adjudicado is highly overall correlated with CUIT and 1 other fieldsHigh correlation
dtotal_articulos_provee is highly overall correlated with CUITHigh correlation
monto_total_adjudicado is highly overall correlated with cant_MontoLimite and 2 other fieldsHigh correlation
periodo_preinscripcion is highly overall correlated with anio_preinscripcion and 1 other fieldsHigh correlation
provincia is highly overall correlated with CUITHigh correlation
Estado is highly imbalanced (65.4%) Imbalance
TipoSocietario is highly imbalanced (94.6%) Imbalance
provincia is highly imbalanced (53.8%) Imbalance
cant_apercibimientos is highly imbalanced (54.2%) Imbalance
cant_representante is highly imbalanced (79.7%) Imbalance
cant_apercibimientos has 2619 (97.9%) missing values Missing
cant_suspensiones has 2637 (98.6%) missing values Missing
cant_antecedentes has 2591 (96.9%) missing values Missing
cant_Apoderado has 830 (31.0%) missing values Missing
cant_representante has 1150 (43.0%) missing values Missing
cant_noAutenticado has 2118 (79.2%) missing values Missing
cant_MontoLimite has 2611 (97.6%) missing values Missing
monto_total_adjudicado is highly skewed (γ1 = 23.10331854) Skewed
CUIT has unique values Unique
Nombre has unique values Unique
antiguedad has 161 (6.0%) zeros Zeros

Reproduction

Analysis started2025-06-18 13:04:37.894482
Analysis finished2025-06-18 13:05:51.878193
Duration1 minute and 13.98 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

CUIT
Real number (ℝ)

High correlation  Unique 

Distinct2674
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0948739 × 1010
Minimum3.0500001 × 1010
Maximum3.4999032 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2025-06-18T10:05:51.956351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3.0500001 × 1010
5-th percentile3.0519469 × 1010
Q13.0644276 × 1010
median3.0708074 × 1010
Q33.0712164 × 1010
95-th percentile3.3707361 × 1010
Maximum3.4999032 × 1010
Range4.499031 × 109
Interquartile range (IQR)67887365

Descriptive statistics

Standard deviation8.8286333 × 108
Coefficient of variation (CV)0.028526633
Kurtosis5.7705058
Mean3.0948739 × 1010
Median Absolute Deviation (MAD)9206068.5
Skewness2.7696292
Sum8.2756928 × 1013
Variance7.7944766 × 1017
MonotonicityNot monotonic
2025-06-18T10:05:52.069788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.070050389 × 10101
 
< 0.1%
3.06448778 × 10101
 
< 0.1%
3.070788251 × 10101
 
< 0.1%
3.371492462 × 10101
 
< 0.1%
3.356160096 × 10101
 
< 0.1%
3.069446559 × 10101
 
< 0.1%
3.059655566 × 10101
 
< 0.1%
3.059015101 × 10101
 
< 0.1%
3.067856116 × 10101
 
< 0.1%
3.067822198 × 10101
 
< 0.1%
Other values (2664) 2664
99.6%
ValueCountFrequency (%)
3.050000112 × 10101
< 0.1%
3.050001091 × 10101
< 0.1%
3.050001107 × 10101
< 0.1%
3.050003462 × 10101
< 0.1%
3.050005062 × 10101
< 0.1%
3.050005116 × 10101
< 0.1%
3.050010632 × 10101
< 0.1%
3.050011313 × 10101
< 0.1%
3.050012415 × 10101
< 0.1%
3.050015094 × 10101
< 0.1%
ValueCountFrequency (%)
3.499903209 × 10101
< 0.1%
3.454668707 × 10101
< 0.1%
3.371740273 × 10101
< 0.1%
3.37169274 × 10101
< 0.1%
3.371671035 × 10101
< 0.1%
3.371646135 × 10101
< 0.1%
3.371638746 × 10101
< 0.1%
3.371584512 × 10101
< 0.1%
3.371568066 × 10101
< 0.1%
3.37156194 × 10101
< 0.1%

Nombre
Text

Unique 

Distinct2674
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size227.5 KiB
2025-06-18T10:05:52.226021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length98
Median length60
Mean length19.253927
Min length3

Characters and Unicode

Total characters51485
Distinct characters93
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2674 ?
Unique (%)100.0%

Sample

1st rowLA BLUSERI S.A.
2nd rowSABADO URSI S.A.
3rd rowSIGNIFY ARGENTINA S.A.
4th rowRognoni y CIA SA
5th rowADSUR S.A..
ValueCountFrequency (%)
s.a 1624
 
19.8%
sa 762
 
9.3%
argentina 199
 
2.4%
de 156
 
1.9%
y 117
 
1.4%
servicios 62
 
0.8%
sociedad 48
 
0.6%
la 45
 
0.5%
a 43
 
0.5%
del 41
 
0.5%
Other values (3472) 5100
62.2%
2025-06-18T10:05:52.495408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 5847
 
11.4%
5523
 
10.7%
S 4420
 
8.6%
. 3481
 
6.8%
E 2775
 
5.4%
I 2614
 
5.1%
O 2235
 
4.3%
R 2155
 
4.2%
N 1914
 
3.7%
C 1714
 
3.3%
Other values (83) 18807
36.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 51485
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 5847
 
11.4%
5523
 
10.7%
S 4420
 
8.6%
. 3481
 
6.8%
E 2775
 
5.4%
I 2614
 
5.1%
O 2235
 
4.3%
R 2155
 
4.2%
N 1914
 
3.7%
C 1714
 
3.3%
Other values (83) 18807
36.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 51485
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 5847
 
11.4%
5523
 
10.7%
S 4420
 
8.6%
. 3481
 
6.8%
E 2775
 
5.4%
I 2614
 
5.1%
O 2235
 
4.3%
R 2155
 
4.2%
N 1914
 
3.7%
C 1714
 
3.3%
Other values (83) 18807
36.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 51485
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 5847
 
11.4%
5523
 
10.7%
S 4420
 
8.6%
. 3481
 
6.8%
E 2775
 
5.4%
I 2614
 
5.1%
O 2235
 
4.3%
R 2155
 
4.2%
N 1914
 
3.7%
C 1714
 
3.3%
Other values (83) 18807
36.5%
Distinct959
Distinct (%)35.9%
Missing0
Missing (%)0.0%
Memory size41.8 KiB
Minimum2016-01-08 00:00:00
Maximum2022-12-07 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-06-18T10:05:52.589150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:52.692208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Estado
Categorical

High correlation  Imbalance 

Distinct8
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size204.8 KiB
Inscripto
2173 
Desactualizado Por Documentos Vencidos
296 
Desactualizado Por Mantencion Formulario
 
75
Pre Inscripto
 
64
Desactualizado Por Clase
 
32
Other values (3)
 
34

Length

Max length40
Median length9
Mean length13.42745
Min length9

Characters and Unicode

Total characters35905
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInscripto
2nd rowInscripto
3rd rowInscripto
4th rowInscripto
5th rowInscripto

Common Values

ValueCountFrequency (%)
Inscripto 2173
81.3%
Desactualizado Por Documentos Vencidos 296
 
11.1%
Desactualizado Por Mantencion Formulario 75
 
2.8%
Pre Inscripto 64
 
2.4%
Desactualizado Por Clase 32
 
1.2%
En Evaluacion 24
 
0.9%
Con Solicitud De Baja 8
 
0.3%
Suspendido 2
 
0.1%

Length

2025-06-18T10:05:52.785951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T10:05:52.867102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
inscripto 2237
56.4%
desactualizado 403
 
10.2%
por 403
 
10.2%
documentos 296
 
7.5%
vencidos 296
 
7.5%
mantencion 75
 
1.9%
formulario 75
 
1.9%
pre 64
 
1.6%
clase 32
 
0.8%
en 24
 
0.6%
Other values (6) 58
 
1.5%

Most occurring characters

ValueCountFrequency (%)
o 4198
11.7%
c 3339
9.3%
s 3266
9.1%
i 3128
8.7%
n 3112
8.7%
t 3019
8.4%
r 2854
7.9%
p 2239
 
6.2%
I 2237
 
6.2%
a 1455
 
4.1%
Other values (18) 7058
19.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 35905
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 4198
11.7%
c 3339
9.3%
s 3266
9.1%
i 3128
8.7%
n 3112
8.7%
t 3019
8.4%
r 2854
7.9%
p 2239
 
6.2%
I 2237
 
6.2%
a 1455
 
4.1%
Other values (18) 7058
19.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 35905
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 4198
11.7%
c 3339
9.3%
s 3266
9.1%
i 3128
8.7%
n 3112
8.7%
t 3019
8.4%
r 2854
7.9%
p 2239
 
6.2%
I 2237
 
6.2%
a 1455
 
4.1%
Other values (18) 7058
19.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 35905
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 4198
11.7%
c 3339
9.3%
s 3266
9.1%
i 3128
8.7%
n 3112
8.7%
t 3019
8.4%
r 2854
7.9%
p 2239
 
6.2%
I 2237
 
6.2%
a 1455
 
4.1%
Other values (18) 7058
19.7%

TipoSocietario
Categorical

High correlation  Imbalance 

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size320.0 KiB
Sociedad Anónima
2635 
Organismo Publico
 
14
Otras Formas Societarias
 
13
Cooperativas
 
5
Unión Transitoria de Empresas
 
5

Length

Max length29
Median length16
Mean length16.063201
Min length12

Characters and Unicode

Total characters42953
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSociedad Anónima
2nd rowSociedad Anónima
3rd rowSociedad Anónima
4th rowSociedad Anónima
5th rowSociedad Anónima

Common Values

ValueCountFrequency (%)
Sociedad Anónima 2635
98.5%
Organismo Publico 14
 
0.5%
Otras Formas Societarias 13
 
0.5%
Cooperativas 5
 
0.2%
Unión Transitoria de Empresas 5
 
0.2%
Sociedades De Hecho 2
 
0.1%

Length

2025-06-18T10:05:52.960842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T10:05:53.038960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
sociedad 2635
49.1%
anónima 2635
49.1%
organismo 14
 
0.3%
publico 14
 
0.3%
otras 13
 
0.2%
formas 13
 
0.2%
societarias 13
 
0.2%
de 7
 
0.1%
unión 5
 
0.1%
cooperativas 5
 
0.1%
Other values (4) 14
 
0.3%

Most occurring characters

ValueCountFrequency (%)
a 5363
12.5%
i 5346
12.4%
n 5299
12.3%
d 5279
12.3%
o 2708
6.3%
2694
6.3%
e 2671
6.2%
m 2667
6.2%
c 2666
6.2%
S 2650
6.2%
Other values (21) 5610
13.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 42953
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 5363
12.5%
i 5346
12.4%
n 5299
12.3%
d 5279
12.3%
o 2708
6.3%
2694
6.3%
e 2671
6.2%
m 2667
6.2%
c 2666
6.2%
S 2650
6.2%
Other values (21) 5610
13.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 42953
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 5363
12.5%
i 5346
12.4%
n 5299
12.3%
d 5279
12.3%
o 2708
6.3%
2694
6.3%
e 2671
6.2%
m 2667
6.2%
c 2666
6.2%
S 2650
6.2%
Other values (21) 5610
13.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 42953
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 5363
12.5%
i 5346
12.4%
n 5299
12.3%
d 5279
12.3%
o 2708
6.3%
2694
6.3%
e 2671
6.2%
m 2667
6.2%
c 2666
6.2%
S 2650
6.2%
Other values (21) 5610
13.1%

periodo_preinscripcion
Real number (ℝ)

High correlation 

Distinct77
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201755.76
Minimum201607
Maximum202211
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2025-06-18T10:05:53.132699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum201607
5-th percentile201610
Q1201701
median201707
Q3201806
95-th percentile202104
Maximum202211
Range604
Interquartile range (IQR)105

Descriptive statistics

Standard deviation142.04414
Coefficient of variation (CV)0.0007040401
Kurtosis1.4332739
Mean201755.76
Median Absolute Deviation (MAD)95
Skewness1.368917
Sum5.394949 × 108
Variance20176.539
MonotonicityNot monotonic
2025-06-18T10:05:53.242062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201611 240
 
9.0%
201701 181
 
6.8%
201612 131
 
4.9%
201703 121
 
4.5%
201610 117
 
4.4%
201706 116
 
4.3%
201705 108
 
4.0%
201704 103
 
3.9%
201702 96
 
3.6%
201708 85
 
3.2%
Other values (67) 1376
51.5%
ValueCountFrequency (%)
201607 12
 
0.4%
201608 54
 
2.0%
201609 57
 
2.1%
201610 117
4.4%
201611 240
9.0%
201612 131
4.9%
201701 181
6.8%
201702 96
 
3.6%
201703 121
4.5%
201704 103
3.9%
ValueCountFrequency (%)
202211 2
 
0.1%
202210 2
 
0.1%
202209 10
0.4%
202208 5
0.2%
202207 6
0.2%
202206 7
0.3%
202205 5
0.2%
202204 7
0.3%
202203 5
0.2%
202202 4
 
0.1%

anio_preinscripcion
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.4903
Minimum2016
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2025-06-18T10:05:53.320179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2016
Q12017
median2017
Q32018
95-th percentile2021
Maximum2022
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4295067
Coefficient of variation (CV)0.00070855692
Kurtosis1.3670489
Mean2017.4903
Median Absolute Deviation (MAD)1
Skewness1.3326323
Sum5394769
Variance2.0434894
MonotonicityNot monotonic
2025-06-18T10:05:53.382676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2017 1136
42.5%
2016 611
22.8%
2018 450
 
16.8%
2019 176
 
6.6%
2020 140
 
5.2%
2021 105
 
3.9%
2022 56
 
2.1%
ValueCountFrequency (%)
2016 611
22.8%
2017 1136
42.5%
2018 450
 
16.8%
2019 176
 
6.6%
2020 140
 
5.2%
2021 105
 
3.9%
2022 56
 
2.1%
ValueCountFrequency (%)
2022 56
 
2.1%
2021 105
 
3.9%
2020 140
 
5.2%
2019 176
 
6.6%
2018 450
 
16.8%
2017 1136
42.5%
2016 611
22.8%

cant_procesos_adjudicado
Real number (ℝ)

High correlation 

Distinct138
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.498878
Minimum1
Maximum1102
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2025-06-18T10:05:53.477906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q38.75
95-th percentile47.35
Maximum1102
Range1101
Interquartile range (IQR)7.75

Descriptive statistics

Standard deviation43.058572
Coefficient of variation (CV)3.4449949
Kurtosis247.98804
Mean12.498878
Median Absolute Deviation (MAD)2
Skewness13.06561
Sum33422
Variance1854.0406
MonotonicityNot monotonic
2025-06-18T10:05:53.571757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 823
30.8%
2 430
16.1%
3 244
 
9.1%
4 170
 
6.4%
6 107
 
4.0%
5 102
 
3.8%
7 70
 
2.6%
8 59
 
2.2%
9 52
 
1.9%
10 46
 
1.7%
Other values (128) 571
21.4%
ValueCountFrequency (%)
1 823
30.8%
2 430
16.1%
3 244
 
9.1%
4 170
 
6.4%
5 102
 
3.8%
6 107
 
4.0%
7 70
 
2.6%
8 59
 
2.2%
9 52
 
1.9%
10 46
 
1.7%
ValueCountFrequency (%)
1102 1
< 0.1%
864 1
< 0.1%
649 1
< 0.1%
455 1
< 0.1%
435 1
< 0.1%
433 1
< 0.1%
389 1
< 0.1%
384 1
< 0.1%
365 1
< 0.1%
298 1
< 0.1%

monto_total_adjudicado
Real number (ℝ)

High correlation  Skewed 

Distinct2653
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9784412 × 108
Minimum0
Maximum4.617215 × 1010
Zeros18
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2025-06-18T10:05:53.681101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile62070.031
Q11254606.7
median8040377.8
Q339101073
95-th percentile6.8888094 × 108
Maximum4.617215 × 1010
Range4.617215 × 1010
Interquartile range (IQR)37846466

Descriptive statistics

Standard deviation1.2607215 × 109
Coefficient of variation (CV)6.3722972
Kurtosis727.60761
Mean1.9784412 × 108
Median Absolute Deviation (MAD)7815727.7
Skewness23.103319
Sum5.2903518 × 1011
Variance1.5894188 × 1018
MonotonicityNot monotonic
2025-06-18T10:05:53.775730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 18
 
0.7%
5666100 2
 
0.1%
534820 2
 
0.1%
9180000 2
 
0.1%
637500 2
 
0.1%
4983272.674 1
 
< 0.1%
18793710.22 1
 
< 0.1%
2020892.185 1
 
< 0.1%
781896973.6 1
 
< 0.1%
6523337.4 1
 
< 0.1%
Other values (2643) 2643
98.8%
ValueCountFrequency (%)
0 18
0.7%
1.7 1
 
< 0.1%
9.471428571 1
 
< 0.1%
59.49779221 1
 
< 0.1%
70.08857143 1
 
< 0.1%
141.24 1
 
< 0.1%
143.65 1
 
< 0.1%
448 1
 
< 0.1%
594.0188571 1
 
< 0.1%
1978.56 1
 
< 0.1%
ValueCountFrequency (%)
4.617215015 × 10101
< 0.1%
2.22605735 × 10101
< 0.1%
1.917565525 × 10101
< 0.1%
1.397951455 × 10101
< 0.1%
1.219233471 × 10101
< 0.1%
1.12638198 × 10101
< 0.1%
7675861678 1
< 0.1%
6933819091 1
< 0.1%
6782343325 1
< 0.1%
6458785714 1
< 0.1%

antiguedad
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5306657
Minimum0
Maximum5
Zeros161
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2025-06-18T10:05:53.857083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median4
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3693215
Coefficient of variation (CV)0.38783663
Kurtosis0.6935806
Mean3.5306657
Median Absolute Deviation (MAD)1
Skewness-1.1661997
Sum9441
Variance1.8750413
MonotonicityNot monotonic
2025-06-18T10:05:53.919668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 1136
42.5%
5 611
22.8%
3 450
 
16.8%
2 176
 
6.6%
0 161
 
6.0%
1 140
 
5.2%
ValueCountFrequency (%)
0 161
 
6.0%
1 140
 
5.2%
2 176
 
6.6%
3 450
 
16.8%
4 1136
42.5%
5 611
22.8%
ValueCountFrequency (%)
5 611
22.8%
4 1136
42.5%
3 450
 
16.8%
2 176
 
6.6%
1 140
 
5.2%
0 161
 
6.0%

provincia
Categorical

High correlation  Imbalance 

Distinct25
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size323.4 KiB
Ciudad Autónoma de Buenos Aires
1601 
Buenos Aires
496 
Córdoba
 
153
Santa Fe
 
107
Mendoza
 
80
Other values (20)
237 

Length

Max length31
Median length31
Mean length22.493642
Min length5

Characters and Unicode

Total characters60148
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCiudad Autónoma de Buenos Aires
2nd rowCiudad Autónoma de Buenos Aires
3rd rowBuenos Aires
4th rowBuenos Aires
5th rowCiudad Autónoma de Buenos Aires

Common Values

ValueCountFrequency (%)
Ciudad Autónoma de Buenos Aires 1601
59.9%
Buenos Aires 496
 
18.5%
Córdoba 153
 
5.7%
Santa Fe 107
 
4.0%
Mendoza 80
 
3.0%
Tierra del Fuego 21
 
0.8%
Chubut 21
 
0.8%
Entre Rios 20
 
0.7%
Chaco 19
 
0.7%
San Juan 17
 
0.6%
Other values (15) 139
 
5.2%

Length

2025-06-18T10:05:53.997781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
aires 2097
21.3%
buenos 2097
21.3%
ciudad 1601
16.3%
de 1601
16.3%
autónoma 1601
16.3%
córdoba 153
 
1.6%
santa 120
 
1.2%
fe 107
 
1.1%
mendoza 80
 
0.8%
san 28
 
0.3%
Other values (27) 342
 
3.5%

Most occurring characters

ValueCountFrequency (%)
7153
11.9%
e 6148
10.2%
u 5467
9.1%
d 5070
8.4%
s 4292
 
7.1%
o 4071
 
6.8%
n 4033
 
6.7%
a 3854
 
6.4%
i 3825
 
6.4%
A 3698
 
6.1%
Other values (28) 12537
20.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 60148
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7153
11.9%
e 6148
10.2%
u 5467
9.1%
d 5070
8.4%
s 4292
 
7.1%
o 4071
 
6.8%
n 4033
 
6.7%
a 3854
 
6.4%
i 3825
 
6.4%
A 3698
 
6.1%
Other values (28) 12537
20.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 60148
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7153
11.9%
e 6148
10.2%
u 5467
9.1%
d 5070
8.4%
s 4292
 
7.1%
o 4071
 
6.8%
n 4033
 
6.7%
a 3854
 
6.4%
i 3825
 
6.4%
A 3698
 
6.1%
Other values (28) 12537
20.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 60148
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7153
11.9%
e 6148
10.2%
u 5467
9.1%
d 5070
8.4%
s 4292
 
7.1%
o 4071
 
6.8%
n 4033
 
6.7%
a 3854
 
6.4%
i 3825
 
6.4%
A 3698
 
6.1%
Other values (28) 12537
20.8%

cant_socios
Real number (ℝ)

High correlation 

Distinct15
Distinct (%)0.6%
Missing14
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2.3206767
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2025-06-18T10:05:54.068354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile5
Maximum18
Range17
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5646363
Coefficient of variation (CV)0.67421555
Kurtosis16.229239
Mean2.3206767
Median Absolute Deviation (MAD)1
Skewness2.8710002
Sum6173
Variance2.4480868
MonotonicityNot monotonic
2025-06-18T10:05:54.146471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2 935
35.0%
1 873
32.6%
3 428
16.0%
4 218
 
8.2%
5 115
 
4.3%
6 44
 
1.6%
7 19
 
0.7%
10 6
 
0.2%
8 6
 
0.2%
9 6
 
0.2%
Other values (5) 10
 
0.4%
(Missing) 14
 
0.5%
ValueCountFrequency (%)
1 873
32.6%
2 935
35.0%
3 428
16.0%
4 218
 
8.2%
5 115
 
4.3%
6 44
 
1.6%
7 19
 
0.7%
8 6
 
0.2%
9 6
 
0.2%
10 6
 
0.2%
ValueCountFrequency (%)
18 1
 
< 0.1%
17 2
 
0.1%
14 3
 
0.1%
13 1
 
< 0.1%
11 3
 
0.1%
10 6
 
0.2%
9 6
 
0.2%
8 6
 
0.2%
7 19
0.7%
6 44
1.6%

cant_apercibimientos
Categorical

High correlation  Imbalance  Missing 

Distinct3
Distinct (%)5.5%
Missing2619
Missing (%)97.9%
Memory size167.3 KiB
1.0
46 
2.0
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters165
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.8%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 46
 
1.7%
2.0 8
 
0.3%
3.0 1
 
< 0.1%
(Missing) 2619
97.9%

Length

2025-06-18T10:05:54.224587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T10:05:54.271458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 46
83.6%
2.0 8
 
14.5%
3.0 1
 
1.8%

Most occurring characters

ValueCountFrequency (%)
. 55
33.3%
0 55
33.3%
1 46
27.9%
2 8
 
4.8%
3 1
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 165
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 55
33.3%
0 55
33.3%
1 46
27.9%
2 8
 
4.8%
3 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 165
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 55
33.3%
0 55
33.3%
1 46
27.9%
2 8
 
4.8%
3 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 165
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 55
33.3%
0 55
33.3%
1 46
27.9%
2 8
 
4.8%
3 1
 
0.6%

cant_suspensiones
Categorical

High correlation  Missing 

Distinct3
Distinct (%)8.1%
Missing2637
Missing (%)98.6%
Memory size167.3 KiB
1.0
19 
2.0
16 
3.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters111
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 19
 
0.7%
2.0 16
 
0.6%
3.0 2
 
0.1%
(Missing) 2637
98.6%

Length

2025-06-18T10:05:54.333951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T10:05:54.396445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 19
51.4%
2.0 16
43.2%
3.0 2
 
5.4%

Most occurring characters

ValueCountFrequency (%)
. 37
33.3%
0 37
33.3%
1 19
17.1%
2 16
14.4%
3 2
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 111
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 37
33.3%
0 37
33.3%
1 19
17.1%
2 16
14.4%
3 2
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 111
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 37
33.3%
0 37
33.3%
1 19
17.1%
2 16
14.4%
3 2
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 111
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 37
33.3%
0 37
33.3%
1 19
17.1%
2 16
14.4%
3 2
 
1.8%

cant_antecedentes
Categorical

High correlation  Missing 

Distinct4
Distinct (%)4.8%
Missing2591
Missing (%)96.9%
Memory size167.4 KiB
1.0
50 
2.0
23 
3.0
5.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters249
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 50
 
1.9%
2.0 23
 
0.9%
3.0 8
 
0.3%
5.0 2
 
0.1%
(Missing) 2591
96.9%

Length

2025-06-18T10:05:54.459845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T10:05:54.507351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 50
60.2%
2.0 23
27.7%
3.0 8
 
9.6%
5.0 2
 
2.4%

Most occurring characters

ValueCountFrequency (%)
. 83
33.3%
0 83
33.3%
1 50
20.1%
2 23
 
9.2%
3 8
 
3.2%
5 2
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 249
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 83
33.3%
0 83
33.3%
1 50
20.1%
2 23
 
9.2%
3 8
 
3.2%
5 2
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 249
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 83
33.3%
0 83
33.3%
1 50
20.1%
2 23
 
9.2%
3 8
 
3.2%
5 2
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 249
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 83
33.3%
0 83
33.3%
1 50
20.1%
2 23
 
9.2%
3 8
 
3.2%
5 2
 
0.8%

cant_Apoderado
Real number (ℝ)

High correlation  Missing 

Distinct12
Distinct (%)0.7%
Missing830
Missing (%)31.0%
Infinite0
Infinite (%)0.0%
Mean1.4755965
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2025-06-18T10:05:54.875775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum12
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0121084
Coefficient of variation (CV)0.6858978
Kurtosis22.07857
Mean1.4755965
Median Absolute Deviation (MAD)0
Skewness3.7821725
Sum2721
Variance1.0243635
MonotonicityNot monotonic
2025-06-18T10:05:54.938268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 1321
49.4%
2 330
 
12.3%
3 111
 
4.2%
4 51
 
1.9%
5 16
 
0.6%
8 5
 
0.2%
7 5
 
0.2%
11 1
 
< 0.1%
9 1
 
< 0.1%
6 1
 
< 0.1%
Other values (2) 2
 
0.1%
(Missing) 830
31.0%
ValueCountFrequency (%)
1 1321
49.4%
2 330
 
12.3%
3 111
 
4.2%
4 51
 
1.9%
5 16
 
0.6%
6 1
 
< 0.1%
7 5
 
0.2%
8 5
 
0.2%
9 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
12 1
 
< 0.1%
11 1
 
< 0.1%
10 1
 
< 0.1%
9 1
 
< 0.1%
8 5
 
0.2%
7 5
 
0.2%
6 1
 
< 0.1%
5 16
 
0.6%
4 51
1.9%
3 111
4.2%

cant_representante
Categorical

High correlation  Imbalance  Missing 

Distinct5
Distinct (%)0.3%
Missing1150
Missing (%)43.0%
Memory size173.1 KiB
1.0
1396 
2.0
 
113
3.0
 
10
4.0
 
4
5.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4572
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row1.0
2nd row1.0
3rd row2.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 1396
52.2%
2.0 113
 
4.2%
3.0 10
 
0.4%
4.0 4
 
0.1%
5.0 1
 
< 0.1%
(Missing) 1150
43.0%

Length

2025-06-18T10:05:55.016470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T10:05:55.094587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1396
91.6%
2.0 113
 
7.4%
3.0 10
 
0.7%
4.0 4
 
0.3%
5.0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
. 1524
33.3%
0 1524
33.3%
1 1396
30.5%
2 113
 
2.5%
3 10
 
0.2%
4 4
 
0.1%
5 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4572
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 1524
33.3%
0 1524
33.3%
1 1396
30.5%
2 113
 
2.5%
3 10
 
0.2%
4 4
 
0.1%
5 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4572
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 1524
33.3%
0 1524
33.3%
1 1396
30.5%
2 113
 
2.5%
3 10
 
0.2%
4 4
 
0.1%
5 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4572
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 1524
33.3%
0 1524
33.3%
1 1396
30.5%
2 113
 
2.5%
3 10
 
0.2%
4 4
 
0.1%
5 1
 
< 0.1%

cant_autenticado
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)0.3%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1.3454341
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2025-06-18T10:05:55.157077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum11
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.71517862
Coefficient of variation (CV)0.53155974
Kurtosis25.57884
Mean1.3454341
Median Absolute Deviation (MAD)0
Skewness3.6659121
Sum3595
Variance0.51148046
MonotonicityNot monotonic
2025-06-18T10:05:55.219572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 1978
74.0%
2 543
 
20.3%
3 106
 
4.0%
4 29
 
1.1%
5 9
 
0.3%
6 4
 
0.1%
9 1
 
< 0.1%
11 1
 
< 0.1%
8 1
 
< 0.1%
(Missing) 2
 
0.1%
ValueCountFrequency (%)
1 1978
74.0%
2 543
 
20.3%
3 106
 
4.0%
4 29
 
1.1%
5 9
 
0.3%
6 4
 
0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
11 1
 
< 0.1%
9 1
 
< 0.1%
8 1
 
< 0.1%
6 4
 
0.1%
5 9
 
0.3%
4 29
 
1.1%
3 106
 
4.0%
2 543
 
20.3%
1 1978
74.0%

cant_noAutenticado
Real number (ℝ)

High correlation  Missing 

Distinct9
Distinct (%)1.6%
Missing2118
Missing (%)79.2%
Infinite0
Infinite (%)0.0%
Mean1.4370504
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2025-06-18T10:05:55.297687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum10
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0034147
Coefficient of variation (CV)0.69824601
Kurtosis18.659656
Mean1.4370504
Median Absolute Deviation (MAD)0
Skewness3.7228597
Sum799
Variance1.006841
MonotonicityNot monotonic
2025-06-18T10:05:55.360186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 414
 
15.5%
2 91
 
3.4%
3 31
 
1.2%
6 6
 
0.2%
4 6
 
0.2%
5 5
 
0.2%
10 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
(Missing) 2118
79.2%
ValueCountFrequency (%)
1 414
15.5%
2 91
 
3.4%
3 31
 
1.2%
4 6
 
0.2%
5 5
 
0.2%
6 6
 
0.2%
7 1
 
< 0.1%
8 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%
6 6
 
0.2%
5 5
 
0.2%
4 6
 
0.2%
3 31
 
1.2%
2 91
 
3.4%
1 414
15.5%

cant_sinMontoLimite
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)0.5%
Missing26
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean1.6276435
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2025-06-18T10:05:55.431112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum12
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0112433
Coefficient of variation (CV)0.62129285
Kurtosis16.98202
Mean1.6276435
Median Absolute Deviation (MAD)0
Skewness3.0851068
Sum4310
Variance1.022613
MonotonicityNot monotonic
2025-06-18T10:05:55.513554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 1550
58.0%
2 768
28.7%
3 200
 
7.5%
4 85
 
3.2%
5 21
 
0.8%
6 11
 
0.4%
8 4
 
0.1%
7 4
 
0.1%
11 2
 
0.1%
9 1
 
< 0.1%
Other values (2) 2
 
0.1%
(Missing) 26
 
1.0%
ValueCountFrequency (%)
1 1550
58.0%
2 768
28.7%
3 200
 
7.5%
4 85
 
3.2%
5 21
 
0.8%
6 11
 
0.4%
7 4
 
0.1%
8 4
 
0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
12 1
 
< 0.1%
11 2
 
0.1%
10 1
 
< 0.1%
9 1
 
< 0.1%
8 4
 
0.1%
7 4
 
0.1%
6 11
 
0.4%
5 21
 
0.8%
4 85
3.2%
3 200
7.5%

cant_MontoLimite
Categorical

High correlation  Missing 

Distinct4
Distinct (%)6.3%
Missing2611
Missing (%)97.6%
Memory size167.4 KiB
1.0
47 
2.0
12 
3.0
 
3
4.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters189
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.6%

Sample

1st row1.0
2nd row1.0
3rd row2.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 47
 
1.8%
2.0 12
 
0.4%
3.0 3
 
0.1%
4.0 1
 
< 0.1%
(Missing) 2611
97.6%

Length

2025-06-18T10:05:55.591671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T10:05:55.654167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 47
74.6%
2.0 12
 
19.0%
3.0 3
 
4.8%
4.0 1
 
1.6%

Most occurring characters

ValueCountFrequency (%)
. 63
33.3%
0 63
33.3%
1 47
24.9%
2 12
 
6.3%
3 3
 
1.6%
4 1
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 189
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 63
33.3%
0 63
33.3%
1 47
24.9%
2 12
 
6.3%
3 3
 
1.6%
4 1
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 189
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 63
33.3%
0 63
33.3%
1 47
24.9%
2 12
 
6.3%
3 3
 
1.6%
4 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 189
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 63
33.3%
0 63
33.3%
1 47
24.9%
2 12
 
6.3%
3 3
 
1.6%
4 1
 
0.5%

total_articulos_provee
Real number (ℝ)

Distinct314
Distinct (%)11.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.734106
Minimum1
Maximum3686
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2025-06-18T10:05:55.732283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median10
Q338
95-th percentile237.05
Maximum3686
Range3685
Interquartile range (IQR)35

Descriptive statistics

Standard deviation177.0112
Coefficient of variation (CV)3.2340201
Kurtosis169.16071
Mean54.734106
Median Absolute Deviation (MAD)9
Skewness10.849539
Sum146359
Variance31332.964
MonotonicityNot monotonic
2025-06-18T10:05:55.841645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 466
 
17.4%
2 192
 
7.2%
3 148
 
5.5%
4 139
 
5.2%
5 103
 
3.9%
6 80
 
3.0%
8 76
 
2.8%
7 67
 
2.5%
11 53
 
2.0%
10 44
 
1.6%
Other values (304) 1306
48.8%
ValueCountFrequency (%)
1 466
17.4%
2 192
7.2%
3 148
 
5.5%
4 139
 
5.2%
5 103
 
3.9%
6 80
 
3.0%
7 67
 
2.5%
8 76
 
2.8%
9 43
 
1.6%
10 44
 
1.6%
ValueCountFrequency (%)
3686 1
< 0.1%
3210 1
< 0.1%
3109 1
< 0.1%
2566 1
< 0.1%
2339 1
< 0.1%
1699 1
< 0.1%
1349 1
< 0.1%
1326 1
< 0.1%
940 1
< 0.1%
939 1
< 0.1%

dmonto_total_adjudicado
Categorical

High correlation 

Distinct19
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size236.2 KiB
(222964579.98, 46172150151.0]
351 
(89439449.702, 222964579.98]
246 
(30451916.51, 46718747.516]
189 
(13557176.81, 19975532.58]
172 
(19975532.58, 30451916.51]
165 
Other values (14)
1551 

Length

Max length29
Median length28
Mean length25.446896
Min length19

Characters and Unicode

Total characters68045
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(3396600.0, 4727330.113]
2nd row(222964579.98, 46172150151.0]
3rd row(13557176.81, 19975532.58]
4th row(2483085.385, 3396600.0]
5th row(30451916.51, 46718747.516]

Common Values

ValueCountFrequency (%)
(222964579.98, 46172150151.0] 351
 
13.1%
(89439449.702, 222964579.98] 246
 
9.2%
(30451916.51, 46718747.516] 189
 
7.1%
(13557176.81, 19975532.58] 172
 
6.4%
(19975532.58, 30451916.51] 165
 
6.2%
(9424898.401, 13557176.81] 147
 
5.5%
(4727330.113, 6702697.888] 139
 
5.2%
(6702697.888, 9424898.401] 138
 
5.2%
(3396600.0, 4727330.113] 129
 
4.8%
(2483085.385, 3396600.0] 120
 
4.5%
Other values (9) 878
32.8%

Length

2025-06-18T10:05:55.936064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
222964579.98 597
 
11.2%
30451916.51 354
 
6.6%
46172150151.0 351
 
6.6%
19975532.58 337
 
6.3%
13557176.81 319
 
6.0%
9424898.401 285
 
5.3%
6702697.888 277
 
5.2%
4727330.113 268
 
5.0%
3396600.0 249
 
4.7%
89439449.702 246
 
4.6%
Other values (11) 2065
38.6%

Most occurring characters

ValueCountFrequency (%)
1 6763
9.9%
9 6428
9.4%
7 5917
8.7%
5 5731
 
8.4%
. 5348
 
7.9%
8 4929
 
7.2%
0 4791
 
7.0%
2 4705
 
6.9%
3 4690
 
6.9%
4 4107
 
6.0%
Other values (6) 14636
21.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 68045
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 6763
9.9%
9 6428
9.4%
7 5917
8.7%
5 5731
 
8.4%
. 5348
 
7.9%
8 4929
 
7.2%
0 4791
 
7.0%
2 4705
 
6.9%
3 4690
 
6.9%
4 4107
 
6.0%
Other values (6) 14636
21.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 68045
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 6763
9.9%
9 6428
9.4%
7 5917
8.7%
5 5731
 
8.4%
. 5348
 
7.9%
8 4929
 
7.2%
0 4791
 
7.0%
2 4705
 
6.9%
3 4690
 
6.9%
4 4107
 
6.0%
Other values (6) 14636
21.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 68045
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 6763
9.9%
9 6428
9.4%
7 5917
8.7%
5 5731
 
8.4%
. 5348
 
7.9%
8 4929
 
7.2%
0 4791
 
7.0%
2 4705
 
6.9%
3 4690
 
6.9%
4 4107
 
6.0%
Other values (6) 14636
21.5%

dcant_procesos_adjudicado
Categorical

High correlation 

Distinct10
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size199.8 KiB
(0.999, 2.0]
1253 
(2.0, 3.0]
244 
(19.0, 39.0]
178 
(3.0, 4.0]
170 
(8.0, 12.0]
167 
Other values (5)
662 

Length

Max length14
Median length12
Mean length11.497756
Min length10

Characters and Unicode

Total characters30745
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(4.0, 5.0]
2nd row(39.0, 1214.0]
3rd row(0.999, 2.0]
4th row(3.0, 4.0]
5th row(5.0, 6.0]

Common Values

ValueCountFrequency (%)
(0.999, 2.0] 1253
46.9%
(2.0, 3.0] 244
 
9.1%
(19.0, 39.0] 178
 
6.7%
(3.0, 4.0] 170
 
6.4%
(8.0, 12.0] 167
 
6.2%
(39.0, 1214.0] 164
 
6.1%
(12.0, 19.0] 160
 
6.0%
(6.0, 8.0] 129
 
4.8%
(5.0, 6.0] 107
 
4.0%
(4.0, 5.0] 102
 
3.8%

Length

2025-06-18T10:05:56.014181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T10:05:56.100918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0 1497
28.0%
0.999 1253
23.4%
3.0 414
 
7.7%
39.0 342
 
6.4%
19.0 338
 
6.3%
12.0 327
 
6.1%
8.0 296
 
5.5%
4.0 272
 
5.1%
6.0 236
 
4.4%
5.0 209
 
3.9%

Most occurring characters

ValueCountFrequency (%)
0 5348
17.4%
. 5348
17.4%
9 4439
14.4%
( 2674
8.7%
, 2674
8.7%
2674
8.7%
] 2674
8.7%
2 1988
 
6.5%
1 993
 
3.2%
3 756
 
2.5%
Other values (4) 1177
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30745
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5348
17.4%
. 5348
17.4%
9 4439
14.4%
( 2674
8.7%
, 2674
8.7%
2674
8.7%
] 2674
8.7%
2 1988
 
6.5%
1 993
 
3.2%
3 756
 
2.5%
Other values (4) 1177
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30745
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5348
17.4%
. 5348
17.4%
9 4439
14.4%
( 2674
8.7%
, 2674
8.7%
2674
8.7%
] 2674
8.7%
2 1988
 
6.5%
1 993
 
3.2%
3 756
 
2.5%
Other values (4) 1177
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30745
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5348
17.4%
. 5348
17.4%
9 4439
14.4%
( 2674
8.7%
, 2674
8.7%
2674
8.7%
] 2674
8.7%
2 1988
 
6.5%
1 993
 
3.2%
3 756
 
2.5%
Other values (4) 1177
 
3.8%

dtotal_articulos_provee
Categorical

High correlation 

Distinct15
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size200.3 KiB
(0.999, 2.0]
658 
(4.0, 6.0]
183 
(29.0, 40.0]
174 
(15.0, 21.0]
172 
(40.0, 58.0]
160 
Other values (10)
1327 

Length

Max length15
Median length12
Mean length11.717277
Min length10

Characters and Unicode

Total characters31332
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(40.0, 58.0]
2nd row(58.0, 97.6]
3rd row(0.999, 2.0]
4th row(58.0, 97.6]
5th row(0.999, 2.0]

Common Values

ValueCountFrequency (%)
(0.999, 2.0] 658
24.6%
(4.0, 6.0] 183
 
6.8%
(29.0, 40.0] 174
 
6.5%
(15.0, 21.0] 172
 
6.4%
(40.0, 58.0] 160
 
6.0%
(2.0, 3.0] 148
 
5.5%
(21.0, 29.0] 146
 
5.5%
(11.0, 15.0] 144
 
5.4%
(6.0, 8.0] 143
 
5.3%
(8.0, 11.0] 140
 
5.2%
Other values (5) 606
22.7%

Length

2025-06-18T10:05:56.225822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2.0 806
15.1%
0.999 658
12.3%
40.0 334
 
6.2%
6.0 326
 
6.1%
4.0 322
 
6.0%
29.0 320
 
6.0%
21.0 318
 
5.9%
15.0 316
 
5.9%
58.0 300
 
5.6%
3.0 287
 
5.4%
Other values (6) 1361
25.4%

Most occurring characters

ValueCountFrequency (%)
0 5413
17.3%
. 5348
17.1%
9 2733
8.7%
( 2674
8.5%
, 2674
8.5%
2674
8.5%
] 2674
8.5%
1 1686
 
5.4%
2 1444
 
4.6%
6 922
 
2.9%
Other values (5) 3090
9.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 31332
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5413
17.3%
. 5348
17.1%
9 2733
8.7%
( 2674
8.5%
, 2674
8.5%
2674
8.5%
] 2674
8.5%
1 1686
 
5.4%
2 1444
 
4.6%
6 922
 
2.9%
Other values (5) 3090
9.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 31332
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5413
17.3%
. 5348
17.1%
9 2733
8.7%
( 2674
8.5%
, 2674
8.5%
2674
8.5%
] 2674
8.5%
1 1686
 
5.4%
2 1444
 
4.6%
6 922
 
2.9%
Other values (5) 3090
9.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 31332
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5413
17.3%
. 5348
17.1%
9 2733
8.7%
( 2674
8.5%
, 2674
8.5%
2674
8.5%
] 2674
8.5%
1 1686
 
5.4%
2 1444
 
4.6%
6 922
 
2.9%
Other values (5) 3090
9.9%

cluster_k_4
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size172.3 KiB
2
2674 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2674
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 2674
100.0%

Length

2025-06-18T10:05:56.306542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T10:05:56.337778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 2674
100.0%

Most occurring characters

ValueCountFrequency (%)
2 2674
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2674
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 2674
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2674
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 2674
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2674
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 2674
100.0%

Interactions

2025-06-18T10:05:47.480616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:04:39.254514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:11.674204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:15.270363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:18.916187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:22.771764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:26.639843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:30.590572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:34.367383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:37.438242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:41.125125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:43.663433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:50.343146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:04:44.119557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:14.288934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:18.145371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:21.791495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:25.697315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:29.650712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:33.417606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:36.555175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:40.257045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:42.826308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:46.507911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:50.437659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:04:46.726853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:14.382630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:18.224293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:21.885234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:25.791059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:29.744466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:33.511420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:36.633290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:40.335829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:42.904417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:46.601555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:50.520849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:04:49.320563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:14.460827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:18.271162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:21.963348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:25.869251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:29.822575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:33.598091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:36.702111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:40.403849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:42.982533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:46.664048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:50.599110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:04:52.004900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:14.555233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:18.349281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:22.041465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:25.962910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:29.900769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:33.694301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:36.795900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:40.481971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-18T10:05:18.411791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:22.135212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-18T10:04:56.920490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:14.719456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:18.489887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:22.226223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:26.119143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:30.056921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:33.851234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:36.952169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-18T10:05:26.197905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-18T10:05:33.944926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-18T10:05:22.398170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:26.292273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:30.231232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-18T10:05:37.108402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:40.812703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:43.358075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:47.073287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:51.028858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:04.158180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:14.993370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:18.697450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:22.476201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:26.370406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:30.324978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:34.116784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:37.186521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:40.875145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:43.429180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:47.151478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:51.106889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:05.846818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:15.071507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:18.759952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:22.584279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:26.448604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:30.403172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:34.194899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:37.264569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:40.953263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:43.507295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:47.229503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:51.200643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:08.709439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:15.176631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:18.838071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:22.678024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:26.526622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:30.481209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:34.273640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:37.344502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:41.031383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:43.585324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:05:47.354496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-18T10:05:56.415895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
CUITEstadoTipoSocietarioanio_preinscripcionantiguedadcant_Apoderadocant_MontoLimitecant_antecedentescant_apercibimientoscant_autenticadocant_noAutenticadocant_procesos_adjudicadocant_representantecant_sinMontoLimitecant_socioscant_suspensionesdcant_procesos_adjudicadodmonto_total_adjudicadodtotal_articulos_proveemonto_total_adjudicadoperiodo_preinscripcionprovinciatotal_articulos_provee
CUIT1.0001.0001.0000.166-0.166-0.1171.0001.0001.000-0.095-0.100-0.1061.000-0.147-0.1721.0001.0001.0001.000-0.0980.1661.0000.068
Estado1.0001.0000.0380.0950.1010.0000.0000.0530.0000.0720.0000.0000.0270.0280.0000.0000.1110.1000.0800.0000.0880.0000.000
TipoSocietario1.0000.0381.0000.0420.0380.1941.0000.0000.0000.1680.0780.0000.1220.1360.0281.0000.0740.1130.0000.1320.0380.0000.000
anio_preinscripcion0.1660.0950.0421.000-1.000-0.0810.0670.0960.0000.008-0.057-0.3350.015-0.064-0.0830.1510.1460.0960.047-0.2140.9530.070-0.120
antiguedad-0.1660.1010.038-1.0001.0000.0810.0670.0960.000-0.0080.0570.3350.0000.0650.0830.1510.1610.1080.0510.213-0.9530.0770.120
cant_Apoderado-0.1170.0000.194-0.0810.0811.0000.4510.0000.0000.4800.6010.1400.2300.6860.1830.0760.0650.0000.0110.125-0.0980.1090.016
cant_MontoLimite1.0000.0001.0000.0670.0670.4511.0000.8161.0000.0000.2941.0000.3520.0000.5741.0000.2970.0000.1881.0000.0670.0000.000
cant_antecedentes1.0000.0530.0000.0960.0960.0000.8161.0000.5030.1030.0000.0000.2040.0000.0000.5200.2080.2790.0000.0000.0960.0000.000
cant_apercibimientos1.0000.0000.0000.0000.0000.0001.0000.5031.0000.0001.0000.0000.0000.0000.0000.0370.3830.6600.0000.0000.0000.0000.000
cant_autenticado-0.0950.0720.1680.008-0.0080.4800.0000.1030.0001.0000.1040.0700.2420.7050.0900.0000.0420.0000.0000.0660.0160.0000.010
cant_noAutenticado-0.1000.0000.078-0.0570.0570.6010.2940.0001.0000.1041.0000.1280.4520.7090.2150.0000.0550.0000.0000.128-0.0690.000-0.052
cant_procesos_adjudicado-0.1060.0000.000-0.3350.3350.1401.0000.0000.0000.0700.1281.0000.0000.1220.0620.3080.1920.0560.0650.575-0.3610.0000.286
cant_representante1.0000.0270.1220.0150.0000.2300.3520.2040.0000.2420.4520.0001.0000.3210.0320.0000.0280.0000.0000.1220.0150.0000.000
cant_sinMontoLimite-0.1470.0280.136-0.0640.0650.6860.0000.0000.0000.7050.7090.1220.3211.0000.1290.0000.0580.0000.0000.108-0.0650.0000.000
cant_socios-0.1720.0000.028-0.0830.0830.1830.5740.0000.0000.0900.2150.0620.0320.1291.0000.0000.0300.0250.0280.092-0.0870.000-0.020
cant_suspensiones1.0000.0001.0000.1510.1510.0761.0000.5200.0370.0000.0000.3080.0000.0000.0001.0000.5440.3440.0001.0000.1510.0000.000
dcant_procesos_adjudicado1.0000.1110.0740.1460.1610.0650.2970.2080.3830.0420.0550.1920.0280.0580.0300.5441.0000.2040.1120.0760.1340.0000.073
dmonto_total_adjudicado1.0000.1000.1130.0960.1080.0000.0000.2790.6600.0000.0000.0560.0000.0000.0250.3440.2041.0000.0350.0420.0910.0500.000
dtotal_articulos_provee1.0000.0800.0000.0470.0510.0110.1880.0000.0000.0000.0000.0650.0000.0000.0280.0000.1120.0351.0000.0260.0440.0210.360
monto_total_adjudicado-0.0980.0000.132-0.2140.2130.1251.0000.0000.0000.0660.1280.5750.1220.1080.0921.0000.0760.0420.0261.000-0.2350.0000.087
periodo_preinscripcion0.1660.0880.0380.953-0.953-0.0980.0670.0960.0000.016-0.069-0.3610.015-0.065-0.0870.1510.1340.0910.044-0.2351.0000.065-0.131
provincia1.0000.0000.0000.0700.0770.1090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0500.0210.0000.0651.0000.000
total_articulos_provee0.0680.0000.000-0.1200.1200.0160.0000.0000.0000.010-0.0520.2860.0000.000-0.0200.0000.0730.0000.3600.087-0.1310.0001.000

Missing values

2025-06-18T10:05:51.365921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-18T10:05:51.554640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-06-18T10:05:51.757744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CUITNombreFechaPreinscripcionEstadoTipoSocietarioperiodo_preinscripcionanio_preinscripcioncant_procesos_adjudicadomonto_total_adjudicadoantiguedadprovinciacant_socioscant_apercibimientoscant_suspensionescant_antecedentescant_Apoderadocant_representantecant_autenticadocant_noAutenticadocant_sinMontoLimitecant_MontoLimitetotal_articulos_proveedmonto_total_adjudicadodcant_procesos_adjudicadodtotal_articulos_proveecluster_k_4
1030644877805LA BLUSERI S.A.12/09/2016InscriptoSociedad Anónima20160920165.04.224898e+065.0Ciudad Autónoma de Buenos Aires3.0NaNNaNNaNNaN1.01.0NaN1.0NaN51.0(3396600.0, 4727330.113](4.0, 5.0](40.0, 58.0]2
1130707882510SABADO URSI S.A.20/09/2016InscriptoSociedad Anónima201609201680.05.823704e+085.0Ciudad Autónoma de Buenos Aires4.0NaNNaNNaN1.0NaN1.0NaN1.0NaN64.0(222964579.98, 46172150151.0](39.0, 1214.0](58.0, 97.6]2
1533714924619SIGNIFY ARGENTINA S.A.18/10/2016InscriptoSociedad Anónima20161020161.01.943875e+075.0Buenos Aires4.0NaNNaNNaN2.01.01.02.03.0NaN1.0(13557176.81, 19975532.58](0.999, 2.0](0.999, 2.0]2
1733561600959Rognoni y CIA SA21/09/2016InscriptoSociedad Anónima20160920164.02.651773e+065.0Buenos Aires2.0NaNNaNNaNNaN2.02.0NaN2.0NaN71.0(2483085.385, 3396600.0](3.0, 4.0](58.0, 97.6]2
1930694465591ADSUR S.A..22/09/2016InscriptoSociedad Anónima20160920166.04.605003e+075.0Ciudad Autónoma de Buenos Aires2.0NaNNaNNaN1.01.01.01.02.0NaN1.0(30451916.51, 46718747.516](5.0, 6.0](0.999, 2.0]2
2030596555655COAMTRA S.A.16/11/2016Desactualizado Por Documentos VencidosSociedad Anónima20161120161.05.669606e+055.0Buenos Aires1.0NaNNaNNaN1.0NaN1.0NaN1.0NaN1.0(377939.298, 599760.0](0.999, 2.0](0.999, 2.0]2
2330590151013VIDITEC S.A..22/07/2016InscriptoSociedad Anónima201607201647.02.714623e+075.0Ciudad Autónoma de Buenos Aires5.01.0NaN1.01.0NaN1.0NaN1.0NaN113.0(19975532.58, 30451916.51](39.0, 1214.0](97.6, 161.0]2
2430678561165NACION SEGUROS S.A.15/11/2016InscriptoSociedad Anónima20161120161102.06.933819e+095.0Ciudad Autónoma de Buenos Aires5.0NaNNaNNaN1.0NaN1.0NaN1.0NaN26.0(222964579.98, 46172150151.0](39.0, 1214.0](21.0, 29.0]2
2530678221976FÁBRICA ARGENTINA DE AVIONES "BRIG. SAN MARTÍN" S.A..17/11/2016InscriptoSociedad Anónima201611201663.01.917566e+105.0Córdoba4.0NaNNaNNaN1.0NaN1.0NaN1.0NaN14.0(222964579.98, 46172150151.0](39.0, 1214.0](11.0, 15.0]2
2730714749206Ebox S.A.29/09/2016InscriptoSociedad Anónima201609201622.01.224767e+085.0Ciudad Autónoma de Buenos Aires2.0NaNNaNNaNNaN1.01.0NaN1.0NaN124.0(89439449.702, 222964579.98](19.0, 39.0](97.6, 161.0]2
CUITNombreFechaPreinscripcionEstadoTipoSocietarioperiodo_preinscripcionanio_preinscripcioncant_procesos_adjudicadomonto_total_adjudicadoantiguedadprovinciacant_socioscant_apercibimientoscant_suspensionescant_antecedentescant_Apoderadocant_representantecant_autenticadocant_noAutenticadocant_sinMontoLimitecant_MontoLimitetotal_articulos_proveedmonto_total_adjudicadodcant_procesos_adjudicadodtotal_articulos_proveecluster_k_4
1004430708304472DROGUERIA GENESIS S.A23/02/2017InscriptoSociedad Anónima20170220172.01.026534e+074.0Ciudad Autónoma de Buenos Aires2.0NaNNaNNaNNaN2.02.0NaN2.0NaN832.0(9424898.401, 13557176.81](0.999, 2.0](345.0, 6993.0]2
1004530678288299SANIRAP SA25/09/2017InscriptoSociedad Anónima20170920171.03.509673e+074.0Ciudad Autónoma de Buenos Aires2.0NaNNaNNaN1.0NaN1.0NaN1.0NaN36.0(30451916.51, 46718747.516](0.999, 2.0](29.0, 40.0]2
1005130707885595Dirsin Corporation S.A.23/02/2018Desactualizado Por Mantencion FormularioSociedad Anónima20180220181.03.166163e+073.0Ciudad Autónoma de Buenos Aires1.0NaNNaNNaN1.01.02.0NaN2.0NaN1.0(30451916.51, 46718747.516](0.999, 2.0](0.999, 2.0]2
1005430708034378RESTEC ARGENTINA S.A.26/07/2016InscriptoSociedad Anónima20160720161.02.903774e+065.0Ciudad Autónoma de Buenos Aires2.0NaNNaNNaN1.0NaN1.0NaN1.0NaN6.0(2483085.385, 3396600.0](0.999, 2.0](4.0, 6.0]2
1006030561699867Melos Ediciones Musicales S.A.06/09/2017InscriptoSociedad Anónima20170920171.01.133333e+064.0Ciudad Autónoma de Buenos Aires3.0NaNNaNNaN1.0NaN1.0NaN1.0NaN4.0(890758.9, 1302657.558](0.999, 2.0](3.0, 4.0]2
1006133711043069VIA CARGO S.A.21/06/2019InscriptoSociedad Anónima20190620191.02.961003e+052.0Chubut1.0NaNNaNNaNNaN1.01.0NaN1.0NaN3.0(224078.198, 377939.298](0.999, 2.0](2.0, 3.0]2
1006430683655143SERVEC S.A.29/11/2021InscriptoSociedad Anónima20211120211.05.169498e+060.0Ciudad Autónoma de Buenos Aires2.0NaNNaNNaNNaN1.01.0NaN1.0NaN3.0(4727330.113, 6702697.888](0.999, 2.0](2.0, 3.0]2
1006630716032503BIOPAZ S.A.11/06/2021InscriptoSociedad Anónima20210620211.07.886492e+040.0Corrientes2.0NaNNaNNaNNaN1.01.0NaN1.0NaN3.0(33011.111, 104767.373](0.999, 2.0](2.0, 3.0]2
1007230518773743Hotel Astor Sociedad Anonima Comercial25/07/2022InscriptoSociedad Anónima20220720221.01.757476e+070.0Ciudad Autónoma de Buenos Aires2.0NaNNaNNaN1.01.01.01.02.0NaN6.0(13557176.81, 19975532.58](0.999, 2.0](4.0, 6.0]2
1007330700503891GIJON SA07/09/2022InscriptoSociedad Anónima20220920221.01.119596e+070.0Ciudad Autónoma de Buenos Aires3.0NaNNaNNaN1.0NaN1.0NaNNaN1.01.0(9424898.401, 13557176.81](0.999, 2.0](0.999, 2.0]2